Integration of multi-criteria decision-making approaches adapted for quality function deployment: an analytical literature review and future research agenda | Emerald Insight

In this paper, based on the search string, research has been done by the QFD and MCDM keywords on the “Publish or Perish” application considering the Web of Science, Scopus and Google Scholar databases to obtain the relevant articles for bibliographical review analysis. The initial result identified 997 items with a total citation of 38,433 considering the articles published from 2004–2021 in Journal and conference publications. After eliminating the duplicates, 881 articles were retrieved, and after excluding the non-English articles and conferences, 315 items were extracted. After screening the abstracts and whole manuscripts to be sure to not eliminate the relevant articles, the relevancy of the given topic focused on the integration of MCDM and QFD and the high H-index. Finally, 59 articles were referenced as literature review research.

As noted, this paper classifies the relevant literature into the following three categories.

Conventional QFD is more qualitative in terms of the parameters’ importance to develop the QFD for establishing a more precise ranking process the MCDM methods can be employed. Kim et al. (2000) applied MCDM tools to optimize CRs with target value ranking the ECs in the QFD matrices. Since 2004, the hybrid QFD-MCDM methods have received increasing attention from both practical and academic perspectives because of QFD itself as a more qualitative tool and to improve the importance weight which is significant in QFD process matrices for evaluating the criteria and improving the output of the matrices as a quantitative result of the QFD. Alinezhad and Seif (2020) used MCDM to develop the imprecise CRs’ ranking and proposed the decision-making tool to improve supplier selection by prioritizing and comparing them in the QFD technique.

Cui et al. (2021) applied the novel MCDM method namely SWARA (stepwise weight assessment ratio analysis) to the QFD in the manufacturing sector in terms of the large number of criteria which facilitated the data computation rather than traditional MCDM tools and decreased the time-consumption of calculations which if not based on the pairwise comparison doesn’t need to have high consistency rate between the CRs or ECs.

It can be discussed that MCDM techniques are the most applied tools in the QFD because of the importance of evaluating the CRs and ECs. In the HOQ, to improve the ranking precision and decrease the computing and quantifying of the model, MCDM tools became strong combination of QFD. Then, it is important to provide a comprehensive literature review of hybrid QFD-MCDM methods. In previous reviews, various types of QFD and Fuzzy-QFD combinations used uncertainty instead of crisp numbers in the HOQ discussed. In the proposed taxonomy, first, we discussed the QFD-MCDM models which are the combination of QFD and MCDM tools. Then, in the second part, the hybrid models consist of QFD-MCDM and sustainability, uncertainty and other supplemental models with hybrid QFD-MCDM in terms of the applied methodology discussed. In the third classification, the hybrid QFD-MCDM methods are described according to how to apply and application area of the methods.

The objective of the three sections was to separate the method adopted in each study and the area of application in section 2.3. For instance, we mean the approach discussed in section 2.2 and the case study explained in section 2.3. Then, some studies discussed in sections 2.1 and 2.2 overlapping in section 2.3 (proposed method in the corresponding case study).

To eliminate the major production wastes, Devnath et al. (2020) presented a QFD-TOPSIS model to find and prioritize the lean tools. To achieve that, first, the authors discussed the major wastes signs and used the QFD technique to transform them to seven basic wastes and prioritized them according to their relative weights. Afterwards using the TOPSIS technique, the seven wastes were converted into the main cause of wastes (lean tools). It was found that the inventory waste, overproduction and motion are the crucial wastes on the shop floor. Moreover, it was confirmed that the Kanban, cellular manufacturing and Kaizen are the most efficient tools for waste elimination.

Concerning manufacturing applications, Ho et al. (2011) adapted QFD and analytic hierarchy process (AHP) to design and improve the sourcing and rank the suppliers in an automobile manufacturing company. Under QFD-MCDM models situation, Yadav et al. (2017) introduced a hybrid framework based on integration of QFD technique with MCDM tools including AHP, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and PROMETHEE to calculate the importance weights of CNs. The authors compared the proposed methods to find the best combination in selection of the proper product (Bike). In order to find the most related criteria and obtain an optimized solution, Sobhanallahi et al. (2019) introduced a QFD-TOPSIS approach to address supplier selection problems in the IT department of a private financial institution. To accomplish that, first the main criteria to fulfill the QFD phases were determined, and then a four-round Delphi technique was performed to find the most appropriate sub-criteria. Finally, they prioritized the high-rank suppliers. Moreover, they carried out a sensitivity analysis to obtain the most significant sub-criteria sensitivity rate which is the cause of change in alternatives’ rank.

As an initial stage of the QFD, it is crucial to convert CRs into engineering characteristics (ECs) and determine the technical importance of each EC. However, as indicated by many researchers, there are various shortcomings in conventional QFD, which limit its efficiency and potential applications. As the main concern, it can be referred to determination of the CRs’ weights based on customers’ evaluations without having a structured pair-wise comparison among CRs. This issue may lead to an inaccurate ranking of ECs. Moreover, ignoring the decision-maker’s preferences by using a linear aggregation method in the traditional QFD can be considered as the second concern. Since 2004, when MCDM methods were frequently adopted in quality management context, an increasing attention has been drawn from both practical and academic perspectives. In this subsection, the QFD-MCDM methods proposed during the time interval 2004–2021 are discussed as below.

2.2 Hybrid models

Recently, incorporating some other topics such as fuzzy theory, Kano model, DEA, risk optimization and so on to establish hybrid QFD-MCDM methods has been considerably applied by the relevant papers during the time frame 2004–2021. In the following subsection, the integration of such topics into hybrid QFD-MCDM models is presented. At the end of this subsection, the geographical scope undertaken with the physical locations of the proposed research is presented.

To calculate the level of fulfilment of design requirements, Karsak (2004) presented a fuzzy multiple objective programming model that employs imprecise and subjective information inherent in the QFD technique. They utilized linguistic variables to represent the imprecise design information and the relative importance of each design objective. The authors evaluated the efficiency of their proposed fuzzy multiple objective decision analysis by a real-world application. A hybrid two-phase framework by integration of fuzzy analytic network process (FANP), QFD and multi-choice goal programming was proposed by Lee et al. (2010) to select the engineering characteristics (ECs) for product design. To accomplish that, first they considered the interrelationship among factors as well as vagueness in human judgments and incorporated the QFD with the super matrix approach of ANP and the fuzzy set theory to calculate the priorities of ECs. In the second phase, to select the most suitable ECs, they established a multi-choice goal programming model by taking the outcome from the first phase and other additional goals into account. Ultimately, they used a real data example of the product design process of backlight unit in thin film transistor liquid crystal display industry in Taiwan to illustrate the practicality of their proposed method.

Thakkar et al. (2011) proposed a methodology for supply chain planning in small and medium enterprises (SMEs) by integrating QFD, interpretive structural modelling (ISM), ANP and zero-one goal programming (ZOGP) approaches. They confirmed that their proposed decision framework can effectively help the SME managers to improve the supply chain decisions. They elaborated the application of their proposed methodology by a case study of short blasting equipment manufacturer SMEs. Wang (2014) integrated the fuzzy QFD (FQFD) approach into the FMCDM problems. They obtained adjusted criteria weights through relative preference relation instead of multiplying two fuzzy numbers to derive criteria weights in FQFD. Zaim et al. (2014) employed a hybrid ANP-weighted fuzzy methodology to analyse the multifarious relationships between the CRs and technical attributes (TAs) and the relative weights among CRs. For this purpose, they synthesized the renowned capabilities of ANP and fuzzy logic for an effective ranking of the product/service attributes while implementing the QFD approach. Li et al. (2014) proposed a novel integrated MCDM method by combining QFD and TOPSIS technique in fuzzy environment. To accomplish that, they used the intuitionistic fuzzy sets to deal with the linguistic opinions. They provided an example to illustrate the applicability of their proposed method. To improve the effectiveness of the QFD in handling the vague, subjective and limited information, Song et al. (2014) proposed a novel group decision approach for effective prioritizing of the TAs. They took the advantages of the rough set theory (RST) approach for handling the vagueness with less prior information and the grey relational analysis (GRA) technique for structuring the analytical framework and discovering necessary information about the data interactions. They also used the compressor rotor industrial data to express the merit of their proposed approach.

In order to select important elements among a wide range of sustainability indicators and launch performance factors for improving the sustainability of manufacturing SMEs, Hsu et al. (2017) utilized a hybrid methodology based on the QFD approach as the basic structure, fuzzy Delphi method (FDM), modified fuzzy extent analytic hierarchy process (FEAHP) and TOPSIS technique to prioritize the performance factors. Their integrated model helps managers to identify key performance factors and deploy the company’s resources to develop the sustainability of the company. Wu et al. (2017) proposed a hybrid analytical model based on the integration of decision-making trial and evaluation laboratory (DEMATEL) and Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) techniques under hesitant fuzzy environment to obtain the importance ratings of ECs in QFD. The hesitant fuzzy DEMATEL has been used to analyse the interrelationships among CRs and determine their weights and the hesitant fuzzy VIKOR to prioritize ECs. Then, it illustrated the feasibility and practicality of their proposed hybrid framework using industrial data borrowed from the product development of the electric vehicle.

Lee et al. (2017) first discussed the limitations of the conventional QFD such as generalizing the opinions of multiple decision-makers, dealing with a huge amount of subjective data, performing a large dimensional comparison and taking the uncertainty into account. Then, to tackle the mentioned issues, they developed a comprehensive model by integrating QFD with fuzzy set theory and decision-making methodologies, including the Delphi method, DEMATEL and ANP for implementing new product development (NPD) project. The authors validated the performance of their proposed model using a case study of solar cell manufacturer. Fiorenzo et al. (2017) first defined the relationships among CRs and ECs and then employed a consolidated ME-MCDM (multi-expert/multiple criteria decisions making) technique to prioritize the ECs. To accomplish that, it has considered: (1) the relationships among ECs and CRs and (2) the importance of the related CRs. Under uncertain linguistic variables, Peng et al. (2018) proposed a systematic decision-making approach for the QFD approach. In this regard, first, they determined CRs based on the hesitant fuzzy linguistic term sets (HFLTSs) to address doubt in human cognition and thought processes. Second, they defined the tolerance deviation to measure the deviation range of fuzzy linguistic terms for quantitative analysis of CR deviation. Afterwards, to deal with uncertainties, they formulated an information entropy to determine the final importance of design requirements.

Galetto et al. (2018) introduced a multi-expert/multiple criteria decision-making based method that does not require any debatable ordinal to cardinal conversion. They assessed theoretical principles and the robustness of their proposed method by some application examples. To overcome the insufficiencies of the traditional QFD, Huang et al. (2019) proposed a novel QFD approach based on proportional hesitant fuzzy linguistic term sets (PHFLTSs) and prospect theory. Moreover, using the best-worst method (BWM), the relative importance of the CRs is determined. Moreover, an extended prospect theory is utilized to prioritize the identified ECs. Finally, the two practical examples are provided to evaluate the applicability and advantages of the proposed QFD approach by the authors. To solve complex decision problems in supply chain management, Yazdani et al. (2019) proposed a multiple-attribute decision-making (MADM)-based fuzzy QFD methodology. To increase the preciseness and decrease vagueness, they employed the interval valued fuzzy sets and grey relational analysis (GRA) to improve the efficiency of the classical QFD. Moreover, they utilized the grey relational coefficient in their proposed fuzzy QFD to measure the similarity to the ideal solution. To improve new product design process, Kang et al. (2018) developed a hybrid method using the evaluation grid method (EGM) and fuzzy Kano model combined with the FAHP-QFD by evaluating the voice of customers (attractive factors) and translate them to DRs. They employed EGM which uses a design philosophy to create attractive product design based on customer’s privileges. To accomplish that, they transmitted the hierarchical customer preferences obtained from EGM to the QFD matrix. Besides, they used the fuzzy-Kano model to classify the crucial attractive factors.

Yazdani et al. (2020) developed an interval type-2 fuzzy sets (IT2FS) DEMATEL-QFD model to evaluate and rank sustainable supply chain drivers in a group decision-making environment. The authors provided a real research project for eliminating risks in the supply chain related to agricultural production systems to illustrate the application of their proposed fuzzy decision model. Through sensitivity analysis, they confirmed the stability of their proposed model and discussed the advantages of their developed model over the existing ones. Ping et al. (2020) introduced a novel QFD approach by integrating the picture fuzzy linguistic sets (PFLSs) and the evaluation based on distance from average solution (EDAS) methods to rank the ECs. In addition, they utilized a combined weighting method based on the TOPSIS method and maximum entropy theory to obtain the weights of the experts objectively. Finally, they elaborated the application of the proposed model by a real-life example from a product-service system design.

Liu and Cheng (2016) introduced a grey quality function deployment (GQFD) method based on the integration of interval grey numbers, QFD and theory of inventive problem solving (TRIZ) techniques. In addition, they developed a new ranking method to determine the ranking order of interval grey numbers. Finally, they highlighted the advantages of their proposed GQFD method using a real industrial data from a computer peripheral product. Babazadeh (2017) used data envelopment analysis (DEA) method to address the uncertainty caused by different behaviour of QFD team members. To achieve that, they considered each member’s subjective assessment and constructed a novel DEA method in group situation. Afterwards, they transformed the proposed model into a linear programming problem.

For determining the order of ECs in the QFD, Wu et al. (2020) extended a multi-objective optimization model the ratio analysis plus the full multiplicative form (MULTIMOORA) method based on cloud model theory (called C-MULTIMOORA). To accomplish that, first, they converted the linguistic variables obtained by decision-makers into normal clouds and aggregated them using the cloud weighted averaging operator. After that, they calculated the weights of CRs through a maximizing deviation method with incomplete weight information. Finally, using the C-MULTIMOORA method, they obtained the relative importance of ECs. They provided an empirical case study from an electric vehicle manufacturing organization to validate the advantages of their proposed QFD-MCDM model.

Using a multi-phase QFD approach, Tian et al. (2018) introduced a hybrid fuzzy MCDM method to cover the performance evaluation of smart BSPs (bike-sharing programs) considering the customer voices under uncertain conditions. For this purpose, they integrated the fuzzy BWM, fuzzy maximizing deviation method (MDM), and fuzzy multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA). Taking both qualitative and quantitative environmental criteria into account, Babbar and Amin (2018) proposed a model based on QFD and multi-objective mathematical method to select the best suppliers for ranking the orders of a beverage company considering environmental factors. To achieve this, they used a qualitative two-stage QFD method to assess the criteria in supplier selection problem by eliminating the vagueness of human judgments. Afterwards, they adapted a multi-objective mixed-integer linear programming model to determine the beverage order quantity. They considered three methods of weighted-sums, distance and ɛ-constraint to optimize cost, defect rate, carbon emission, weight of suppliers and on-time delivery objectives. Table 2 summarizes the relevant papers in terms of the practical application and MCDM technique.

To overcome the limitations of the traditional QFD, Liu et al. (2019) proposed a novel QFD approach by integrating the extended hesitant fuzzy linguistic term sets (EHFLTSs) and prospect theory. To accomplish that, first, they used the EHFLTSs for the elicitation of hesitant linguistic assessment information of the QFD team members. Then, taking the interrelations between CRs into account, they employed Choquet integral to obtain the aggregated relationship evaluation results. Furthermore, they suggested an extended prospect theory to derive the ranking orders of ECs.

Yazdani et al. (2017) presented a new integrated approach based on the DEMATEL and QFD methods. To this end, a multi-objective optimization based on ratio analysis (MOORA) and complex proportional assessment (COPRAS) methods were applied to rank and compare the green suppliers. Initially, the main CRs defined and were used to obtain the TAs for prioritizing the supplier criteria. The authors also utilized the DEMATEL technique to evaluate the establishment of direct and indirect causal relationships between different customer variables. Afterward, the QFD model was applied to establish a relationship matrix to determine the value of each pair of CRs and the supplier selection criteria. Then, the authors identified the supplier rating matrix. The novelty of this model was to use the MOORA and COPRAS methods to indicate whether one alternative is better or worse than another one.

Gündoğdu and Kahraman (2020) introduced a hybrid spherical fuzzy set (SFS)-QFD technique to address the linguistic evaluations of the criteria importance. They used the SFS to prioritize the CRs and improve the ECs values, and, due to this objective, they aggregated judgments in the HOQ matrix, correlation matrix and customer evaluation matrix by spherical fuzzy aggregation operators. They employed a competitive analysis using the SF-TOPSIS to obtain the final weights of competitors. Haber et al. (2020) developed a hybrid method considering the QFD technique and Kano model integrated with the fuzzy-AHP for the improvement of product-service systems. The Kano model has been applied to transform the CRs and commute them into Receiver State Parameters (RSPs). Then, the authors used the FAHP for reducing the ambiguity regarding the proper understanding of the PSS receivers. To determine the importance weights for engineering characteristics of the product design, Mistarihi et al. (2020) focused on presenting a hybrid FANP-QFD method. To accomplish that, they obtained the relative importance weights of the CRs from fuzzy pairwise comparison matrix by using fuzzy-AHP technique.

Ahmadzadeh et al. (2021) ranked the critical success factors (CSF) of enterprise resources planning (ERP). Firstly, they used the DEMATEL technique to identify the CSFs of ERP and the enablers of organization agility (OA). After that, they established a multi-phase QFD to rank both the influencing and influenced criteria. They showed that the organizational structure, IT technology infrastructure and commitment and support by top managers includes indicators with top priority. Kaya and Erginel (2020) developed a new hybrid method based on the hesitant fuzzy (HS)-QFD using sustainable passenger requirements (SPRs) to design or improve an airport based on sustainable criteria including environmental, social and economic. During the decision-making process, to reflect the hesitancy in human nature, they implemented the HF-SQFD method to rank the design requirements for a sustainable airport. Afterwards, they determined the importance weight of SPRs by employing the hesitant fuzzy stepwise weight assessment ratio analysis (HF-SWARA) method which has the privilege of a less pairwise comparison matrix rather than other MCDM tools. Wang et al. (2020b) used an improved QFD methodology by integration of cloud model and GRA. They implemented the comparative analysis of different approaches as well as the sensitivity analysis on criteria weights to demonstrate the stability of their proposed method.

The research scheme of the selected studies is presented through a geographical scope in Figure 1. In this study, the geographical scope shows that China (15 studies), Iran (6 studies), Turkey and Taiwan (5 studies), Italy (4 studies) and the Philippines (3 studies) are the six countries with the highest number of studies on hybrid QFD-MCDM topics, respectively.